Learning Prosocial Skills Through Multiadaptive Games: A Case Study

Abstract:

Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.”

The focus of the Visual Computing Laboratory is to develop new algorithms and architectures for applications in the areas of 3D processing, image/video processing, computer vision, pattern recognition, bioinformatics and medical imaging.